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Connecting content to community in social media via image content, user tags and user communication

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5 Author(s)
De Choudhury, M. ; Arts, Media & Eng., Arizona State Univ., Tempe, AZ, USA ; Sundaram, H. ; Yu-Ru Lin ; John, A.
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In this paper we develop a recommendation framework to connect image content with communities in online social media. The problem is important because users are looking for useful feedback on their uploaded content, but finding the right community for feedback is challenging for the end user. Social media are characterized by both content and community. Hence, in our approach, we characterize images through three types of features: visual features, user generated text tags, and social interaction (user communication history in the form of comments). A recommendation framework based on learning a latent space representation of the groups is developed to recommend the most likely groups for a given image. The model was tested on a large corpus of Flickr images comprising 15,689 images. Our method outperforms the baseline method, with a mean precision 0.62 and mean recall 0.69. Importantly, we show that fusing image content, text tags with social interaction features outperforms the case of only using image content or tags.

Published in:

Multimedia and Expo, 2009. ICME 2009. IEEE International Conference on

Date of Conference:

June 28 2009-July 3 2009

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